arXiv subjects

Xianjin Liu

Publications and source records attributed to Xianjin Liu.

5 recordsLinked to original sources

arXiv2026

Robust class-gated single-pixel diffractive optical neural network with random-aberration-aware training

Optical computing offers the theoretical potential for high-speed, energy-efficient inference, yet its practical deployment remains constrained by fundamental input-output bottlenecks, particularly the reliance on electronic sensors with limited frame rates and stringent alignment requirements between optical components. Here, we demonstrate an image-class-gated single-pixel DONN that overcomes these limitations by converting spatial complexity into a temporal intensity signature. Using a minimal architecture comprising a reconfigurable digital micromirror device and a single-pixel photodetector, we implement a virtual optical gate. The system time-multiplexes class-specific masks, causing the detector response to peak only when the mask index matches the input class. This allows the predicted label to be read out via peak timing rather than spatial localization, eliminating 2D sensor constraints. To bridge the persistent sim-to-real gap, we introduce a physics-aware training strategy using random-phase augmentation. This method renders the model intrinsically tolerant to phase aberrations and mechanical misalignments without requiring precise hardware modeling. Our prototype achieves 90.0%(MNIST) and 80.0% (Fashion-MNIST) accuracy at a readout rate of 5 kHz. By combining gigahertz-compatible single-pixel detection with robust and alignment-tolerant training, this work provides a scalable, hardware-efficient pathway toward real-time optical intelligent sensing.

physics.optics↗

arXiv2025

Spectro-Polarimetric Holographic Multiplexing Metasurface with Super-High Capacity Empowered by Mechanical Rotation

Mechanically reconfigurable metasurfaces capable of translation, rotation, and permutation have attracted considerable attention for high-capacity optical information storage and full-color holographic displays, owing to their low-power and high functional scalability, despite the additional system-level complexity introduced by precision rotation stages. This study presents a differentiable inverse design framework for such metasurfaces, creating an accurate mapping between meta-atom geometries and their multi-channel optical responses across diverse optical dimensions. Using a deep neural network-driven, end-to-end optimization pipeline, the framework enables intelligent, iterative refinement of rotatable metasurface within constrained design space. Using this approach, we show high-fidelity holographic video display by rotating a single element in a cascaded metasurface doublet around the optical axis. The doublet enables pixel-resolved holographic imaging with 288 independent channels, and by switching input/output polarization states, the system demonstrates four distinct full-color dynamic holographic videos. This work establishes an alternative paradigm for optical parameter multiplexing and end-to-end inverse design in mechanically reconfigurable metasurfaces, suggesting applications in compact optical systems, dynamic holography, information processing, and optical computing.

physics.optics↗

arXiv2025

NTIRE 2025 XGC Quality Assessment Challenge: Methods and Results

This paper reports on the NTIRE 2025 XGC Quality Assessment Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2025. This challenge is to address a major challenge in the field of video and talking head processing. The challenge is divided into three tracks, including user generated video, AI generated video and talking head. The user-generated video track uses the FineVD-GC, which contains 6,284 user generated videos. The user-generated video track has a total of 125 registered participants. A total of 242 submissions are received in the development phase, and 136 submissions are received in the test phase. Finally, 5 participating teams submitted their models and fact sheets. The AI generated video track uses the Q-Eval-Video, which contains 34,029 AI-Generated Videos (AIGVs) generated by 11 popular Text-to-Video (T2V) models. A total of 133 participants have registered in this track. A total of 396 submissions are received in the development phase, and 226 submissions are received in the test phase. Finally, 6 participating teams submitted their models and fact sheets. The talking head track uses the THQA-NTIRE, which contains 12,247 2D and 3D talking heads. A total of 89 participants have registered in this track. A total of 225 submissions are received in the development phase, and 118 submissions are received in the test phase. Finally, 8 participating teams submitted their models and fact sheets. Each participating team in every track has proposed a method that outperforms the baseline, which has contributed to the development of fields in three tracks.

cs.CV↗

arXiv2024

Hypermultiplexed off-chip hologram by on-chip integrated metasurface

The waveguide-integrated metasurface introduces a novel photonic chip capable of converting guided modes into free-space light. This enables functions such as off-chip beam focusing, steering, and imaging. The challenge lies in achieving hypermultiplexing across diverse parameters, including guided-wave mode type, direction, polarization, and notably, multiple wavelengths. Here, we introduce a comprehensive end-to-end inverse design framework, rooted in a physical model, for the multifunctional design of on-chip metasurfaces. This framework allows for metasurface optimization through a target-field-driven iteration process. We demonstrate a hypermultiplexed on-chip metasurface capable of generating red-green-blue holograms at multiple target planes, with both independent and cooperative control over guided-wave direction. Significantly, the proposed method streamlines the design process utilizing only the positions of meta-atoms as the design variable. We demonstrate 9 independent holographic channels through a combination of wavelength and distance multiplexing. Moreover, by incorporating the excitation direction into the design, the metasurface produces a total of 36 distinct holograms. The robustness of these results against fabrication discrepancies is validated through 3D full-wave electromagnetic simulations, aligning well with advanced manufacturing techniques. Our research presents a universal design framework for the development of multifunctional on-chip metasurfaces, opening up new avenues for a wide range of applications.

physics.optics↗

arXiv2024

Toward Real Text Manipulation Detection: New Dataset and New Solution

With the surge in realistic text tampering, detecting fraudulent text in images has gained prominence for maintaining information security. However, the high costs associated with professional text manipulation and annotation limit the availability of real-world datasets, with most relying on synthetic tampering, which inadequately replicates real-world tampering attributes. To address this issue, we present the Real Text Manipulation (RTM) dataset, encompassing 14,250 text images, which include 5,986 manually and 5,258 automatically tampered images, created using a variety of techniques, alongside 3,006 unaltered text images for evaluating solution stability. Our evaluations indicate that existing methods falter in text forgery detection on the RTM dataset. We propose a robust baseline solution featuring a Consistency-aware Aggregation Hub and a Gated Cross Neighborhood-attention Fusion module for efficient multi-modal information fusion, supplemented by a Tampered-Authentic Contrastive Learning module during training, enriching feature representation distinction. This framework, extendable to other dual-stream architectures, demonstrated notable localization performance improvements of 7.33% and 6.38% on manual and overall manipulations, respectively. Our contributions aim to propel advancements in real-world text tampering detection. Code and dataset will be made available at https://github.com/DrLuo/RTM

cs.CV↗